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Waist circumference does not improve established cardiovascular disease risk prediction modeling.

ABSTRACT: Despite considerable evidence demonstrating that waist circumference (WC) is independently associated with cardiovascular disease (CVD) and/or all-cause mortality, whether the addition of WC improves risk prediction models is unclear. The objective was to evaluate the improvement in risk prediction with the addition of WC, alone or in combination with BMI, to the Framingham Risk Score (FRS) and a population specific model. 34,377 men and 9,477 women aged 20 to 79 years who completed a baseline examination at the Cooper Clinic (Dallas, TX) during 1977-2003 and enrolled in the Aerobics Center Longitudinal Study (ACLS). WC was measured at the level of the umbilicus and expressed as a continuous variable. Deaths among participants were identified using the National Center for Health Statistics National Death Index. A total of 728 fatal cardiovascular disease (CVD) events occurred over a mean follow-up period of 13.1 ± 7.5 years. WC was significantly higher in CVD decedents (P = .002). The FRS C-statistic for fatal CVD in men was 0.836 (0.816-0.855) and 0.883 (0.851-0.915) in women. The addition of WC did not improve the C-statistic in men (0.831 (0.809-0.853)) or women (0.883 (0.850-0.916)). Similar findings were observed for non-fatal CVD and all-cause mortality, and when WC was added to a population specific model. Upon adding WC, the net-reclassification index was 0.024 with an integrated discrimination improvement of -0.0004. The addition of WC, alone or in combination with BMI, did not substantively improve risk prediction for CVD or all-cause mortality compared to the Framingham Risk Score or a population specific model.

SUBMITTER: Nelms MW

PROVIDER: S-EPMC7531816 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Waist circumference does not improve established cardiovascular disease risk prediction modeling.

Nelms Matthew W MW Day Andrew G AG Sui Xuemei X Blair Steven N SN Ross Robert R

PloS one 20201002 10

Despite considerable evidence demonstrating that waist circumference (WC) is independently associated with cardiovascular disease (CVD) and/or all-cause mortality, whether the addition of WC improves risk prediction models is unclear. The objective was to evaluate the improvement in risk prediction with the addition of WC, alone or in combination with BMI, to the Framingham Risk Score (FRS) and a population specific model. 34,377 men and 9,477 women aged 20 to 79 years who completed a baseline e ...[more]

PMID: 33007042

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Project description:BackgroundWaist circumference is becoming recognized as a useful predictor of health risks in clinical research. However, clinical datasets tend to lack this measurement and self-reported values tend to be inaccurate. Predicting waist circumference from standard physical features could be a viable method for generating this information when it is missing or mitigating the impact of inaccurate self-reports. This study determined the degree to which the XGBoost advanced machine learning algorithm could build models that predict waist circumference from height, weight, calculated Body Mass Index, age, race/ethnicity and sex, whether they perform better than current models based on linear regression, and the relative importance of each feature in this prediction.MethodsWe trained tree-based models (via XGBoost gradient boosting) and linear models (via regression) to predict waist circumference from height, weight, Body Mass Index, age, race/ethnicity and sex (n = 60,740 participants). We created 10 iterations of each model, each using 90% of the dataset for training and the remaining 10% for testing performance (this group was different for each iteration). We calculated model performance and feature importance as an average across 10 iterations. We then externally validated the ensembled version of the top model.ResultsThe XGBoost model predicted waist circumference with a mean bias ± standard deviation of 0.0 ± 0.04 cm and a root mean squared error of 4.7 ± 0.05 cm, with performance varying slightly by sex and race/ethnicity. The XGBoost model showed varying degrees of improvement over linear regression models. The top 3 predictors were Body Mass Index, weight and race (Asian). External validation found that on average this model overestimated waist circumference by 4.65 cm in the United Kingdom population (mainly due to overprediction in females) and underestimated waist circumference by 1.7 cm in the Chinese population. The respective root mean squared errors were 7.7 cm and 7.1 cm.ConclusionsXGBoost-based models accurately predict waist circumference from standard physical features. Waist circumference prediction using this approach would be valuable for epidemiological research and beyond.

Project description:BackgroundThe triglyceride and glucose-waist circumference (TyG-WC) index demonstrated a strong association with insulin resistance, especially in Asian population. However, evidence on the association between TyG-WC index and the occurrence of cardiovascular disease (CVD) is limited. This study aimed to verify association between the TyG-WC index and the occurrence of CVD by considering all-cause mortality as a competing risk.MethodsThe study included 7482 participants divided into four groups based on the TyG-WC index quartiles. Kaplan-Meier curves illustrated cumulative incidence rates of CVD and all-cause mortality during the follow-up period. Log-rank tests determined group differences. The Cox proportional hazard spline curve demonstrates the dose-dependent relationship between the TyG-WC index and incident CVD. Modified Cox regression (Fine and Gray) estimated hazard ratios (HRs) with 95% CIs for incident CVD, treating death as a competing risk. Death event after incident CVD was excluded from the death count.ResultsDuring the median 15.94 year of follow-up period, a total of 691 (9.24%) new-onset CVD cases and 562 (7.51%) all-cause mortality cases were confirmed. Cox proportional hazard spline curves suggested that TyG-WC index exhibited a dose-dependent positive correlation with incident CVD. The cumulative incidence rate of CVD was significantly higher in the groups with higher TyG-WC index quartiles in Kaplan-Meier curves. The adjusted HR (95% CI) for incident CVD in Q2-Q4, compared with Q1, was 1.47 (1.12-1.93), 1.91 (1.44-2.54) and 2.24 (1.63-3.07), respectively. There was no significant association between TyG-WC index and all-cause mortality. Specifically, angina and stroke were significantly associated with the TyG-WC index, in contrast to myocardial infarction and peripheral artery disease.ConclusionsThe TyG-WC index was positively associated with incident CVD even considering all-cause mortality as a competing risk. Therefore, TyG-WC index may be a valuable marker for predicting the occurrence of CVD.

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Waist circumference does not improve established cardiovascular disease risk prediction modeling.

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Waist circumference does not improve established cardiovascular disease risk prediction modeling.

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